The disclosure relates generally to power generation, and more particularly to optimization of power generation in a microgrid that includes at least one renewable power source.
In power generation, it is becoming more common for power system assets to include a mixture of renewable and non-renewable power generation sources, particularly in so-called “smart grid” power generation and distribution. Particularly with regard to power systems including renewable energy sources, excess energy may be stored for use during times when power demand exceeds power generation capacity, though in a mixed-generation environment, non-renewable sources may be called upon instead of, or in addition to, stored energy. It is often desirable to optimize use of renewable, non-renewable, and stored energy resources so that they may be used more advantageously.
Generation optimization techniques for microgrid applications typically use load and renewable power generation forecasts to calculate a shortage of power that should be covered by non-renewable sources and/or energy storage devices. Optimization-oriented calculations, referred to as optimal dispatch scheduling, are performed based on a calculated power shortage to determine optimal set points for these devices. In such optimization techniques, a non-renewable source is typically assumed to act as an isochronous source to mitigate frequency deviations and to maintain a consistent system frequency. As used herein, “isochronous” means substantially steady frequency. Thus, an isochronous mode of a power source is a mode in which power is generated to maintain substantially constant frequency over time. An isochronous source or machine in the system may change its output to catch load swing, maintain load-generation balance, and hence, stabilize the frequency and/or mitigate load-generation imbalance. An isochronous machine may perform this task by employing a control system that measures system frequency and adjusts generated power accordingly.
To perform frequency adjustment and/or mitigate load-generation imbalance, an isochronous machine should set a reserve margin aside during normal operation. For example, if the maximum microgrid load is 100 kilowatts (kW) and isochronous source rating is 50 kW, a ±10 kW margin may be set aside from the isochronous source, and the machine may not be permitted to produce more than 40 kW and/or less than 10 kW during normal operation (steady state). For a non-renewable power source like a diesel-powered generator, that upper bound (50 kW) is fixed and achievable; however, for a renewable power source, the upper bound may decrease due to intermittency (dry day for hydroelectric, cloudy day for solar and/or combined solar and energy storage, calm day for wind and/or combined wind and energy storage, etc.), which may lead to infeasibility of the optimization problem. As a result of these and other factors, a model assuming isochronous power generation may not accurately represent the behavior of many renewable power sources since power generation by renewable power sources may fluctuate due to changes in wind speed, cloud cover, water levels, or other environmental factors that might affect a particular renewable power source.
In order to formulate an optimal dispatch technique within a microgrid, a variety of operational limitations and complex constraints should be considered. Depending on the number and nature of devices or assets within a microgrid, these constraints and limitations may render optimization practically unsolvable with currently available computational algorithms and resources, in particular for the fast-response requirements of real-time applications. For example, if power generation optimization is formulated in the form of mixed integer nonlinear programming (MINLP) or mixed integer linear programming (MILP) problems, operational constraints that are typically complex may be considered, but require substantial computing resources and time. In fact, MINLP and MILP analyses present such computational challenges that the use of these techniques may be impractical for real-time and fast-response applications. Conventional linear programming (LP) is a practical technique as far as processing overhead, but may not be suitable since LP can not consider the above-mentioned, complex operational constraints. In addition to this problem, conventional dispatch optimization is typically based on separate integer programming (IP) and LP solution branches, and the potential of energy storage utilization is generally ignored in the IP. This may result in unnecessary commitment of non-renewable power sources, which may result in lost opportunities for storing excess energy that may later be retrieved from storage instead of committing non-renewable units.